Decision support systems have been employed to assist the operational management of complex infrastructures, including those used in supply and distribution of various utilities (gas, water, electricity etc.). These systems use data supplied by various sensors, together with historic data, to make predictions about the infrastructure and to identify any need for intervention. For instance, support vector machines can be used for demand forecasting in a utility network. In such systems, detecting anomalies, both in terms of the actual measurements reported, and the timing at which such reporting takes place (or fails to take place) is an important requirement.
Typically, the readings from the various sensors will be transmitted over a wireless communication network to a central processing facility for analysis. Where issues of wireless connectivity or interference arise in the wireless network, sensor readings may be delayed in reaching the central processing facility, or may be missed entirely. As a result, the accuracy of subsequent predictions becomes worse and the ability to detect anomalies is compromised. It is, therefore, desirable to dynamically optimise the configuration of the wireless communications network, in such a way as to ensure that anomalous events do not go unnoticed.